pacman::p_load(sf, terra, spatstat,
tmap, rvest, tidyverse)Hands-0n Exercise 2A
First-order Spatial Point Patterns Analysis Methods
Data source
The Child care service data is from data.gov.sg and aster Plan 2019 Subzone Boundary (No Sea) is from another source.
Installing and loading some new packages
importing and Wrangling Geospaitial data
mpsz_sf <- st_read("K:/kalpitkulshrestha24/ISSS626/Hands-on_Exercise/Hands-on EX02/data/Geospatial/MasterPlan2019SubzoneBoundaryNoSeaKML.kml") %>%
st_zm(drop = TRUE, what = "ZM") %>% st_transform(crs = 3414)Reading layer `URA_MP19_SUBZONE_NO_SEA_PL' from data source
`K:\kalpitkulshrestha24\ISSS626\Hands-on_Exercise\Hands-on EX02\data\Geospatial\MasterPlan2019SubzoneBoundaryNoSeaKML.kml'
using driver `KML'
Simple feature collection with 332 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS: WGS 84extract_kml_field <- function(html_text, field_name) {
if (is.na(html_text) || html_text == "") return(NA_character_)
page <- read_html(html_text)
rows <- page %>% html_elements("tr")
value <- rows %>%
keep(~ html_text2(html_element(.x, "th")) == field_name) %>%
html_element("td") %>%
html_text2()
if (length(value) == 0) NA_character_ else value
}mpsz_sf <- mpsz_sf %>%
mutate(
REGION_N = map_chr(Description, extract_kml_field, "REGION_N"),
PLN_AREA_N = map_chr(Description, extract_kml_field, "PLN_AREA_N"),
SUBZONE_N = map_chr(Description, extract_kml_field, "SUBZONE_N"),
SUBZONE_C = map_chr(Description, extract_kml_field, "SUBZONE_C")
) %>%
select(-Name, -Description) %>%
relocate(geometry, .after = last_col())mpsz_cl <- mpsz_sf %>%
filter(SUBZONE_N != "SOUTHERN GROUP",
PLN_AREA_N != "WESTERN ISLANDS",
PLN_AREA_N != "NORTH-EASTERN ISLANDS")write_rds(mpsz_cl,
"K:/kalpitkulshrestha24/ISSS626/Hands-on_Exercise/Hands-on EX02/data/GEospatial/mpsz_cl.rds")Importing and loading Child care service data
childcare_sf <- st_read("K:/kalpitkulshrestha24/ISSS626/Hands-on_Exercise/Hands-on EX02/data/Aspatial/ChildCareServices.kml") %>%
st_zm(drop = TRUE, what = "ZM") %>%
st_transform(crs = 3414)Reading layer `CHILDCARE' from data source
`K:\kalpitkulshrestha24\ISSS626\Hands-on_Exercise\Hands-on EX02\data\Aspatial\ChildCareServices.kml'
using driver `KML'
Simple feature collection with 1925 features and 2 fields
Geometry type: POINT
Dimension: XYZ
Bounding box: xmin: 103.6878 ymin: 1.247759 xmax: 103.9897 ymax: 1.462134
z_range: zmin: 0 zmax: 0
Geodetic CRS: WGS 84st_crs(mpsz_cl)Coordinate Reference System:
User input: EPSG:3414
wkt:
PROJCRS["SVY21 / Singapore TM",
BASEGEOGCRS["SVY21",
DATUM["SVY21",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4757]],
CONVERSION["Singapore Transverse Mercator",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["northing (N)",north,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["easting (E)",east,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre, engineering survey, topographic mapping."],
AREA["Singapore - onshore and offshore."],
BBOX[1.13,103.59,1.47,104.07]],
ID["EPSG",3414]]st_crs(childcare_sf)Coordinate Reference System:
User input: EPSG:3414
wkt:
PROJCRS["SVY21 / Singapore TM",
BASEGEOGCRS["SVY21",
DATUM["SVY21",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4757]],
CONVERSION["Singapore Transverse Mercator",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["northing (N)",north,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["easting (E)",east,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre, engineering survey, topographic mapping."],
AREA["Singapore - onshore and offshore."],
BBOX[1.13,103.59,1.47,104.07]],
ID["EPSG",3414]]Mapping the geospatial data
plot(st_geometry(mpsz_cl))
plot(st_geometry(childcare_sf), add = TRUE)
tmap_mode('view')ℹ tmap mode set to "view".tm_shape(childcare_sf)+
tm_dots()Registered S3 method overwritten by 'jsonify':
method from
print.json jsonliteGeospatial Data Wrangling
using specific data structures like ppp(planar point pattern)
Converting sf data frames to ppp class
spatstat will use the point event data in ppp object form using [ as.ppp()] of spatstat package to convert childcare_sf to ppp format.
childcare_ppp <- as.ppp(childcare_sf)class(childcare_ppp)[1] "ppp"summary(childcare_ppp)Marked planar point pattern: 1925 points
Average intensity 2.417323e-06 points per square unit
Coordinates are given to 11 decimal places
Mark variables: Name, Description
Summary:
Name Description
Length:1925 Length:1925
Class :character Class :character
Mode :character Mode :character
Window: rectangle = [11810.03, 45404.24] x [25596.33, 49300.88] units
(33590 x 23700 units)
Window area = 796335000 square unitsCreating owin object
sg_owin <- as.owin(mpsz_cl)class(sg_owin)[1] "owin"plot(sg_owin)
Combining point events object and owin object
childcareSG_ppp = childcare_ppp[sg_owin]childcareSG_pppMarked planar point pattern: 1925 points
Mark variables: Name, Description
window: polygonal boundary
enclosing rectangle: [2667.54, 55941.94] x [21448.47, 50256.33] unitsClark-Evan Test for Nearest Neighbour Analysis
a spatial statistics method that calculates the average distance between each point and its closest neighbor to determine if a pattern of points is clustered, dispersed, or randomly distributed.
The test hypotheses are:
Ho = The distribution of childcare services are randomly distributed.
H1= The distribution of childcare services are not randomly distributed.
The 95% confident interval will be used.
perfromin the Clark-Evans test without CSR
clarkevans.test(childcareSG_ppp, correction = "none", clipregion = "sg_owin", alternative = c("clustered"))
Clark-Evans test
No edge correction
Z-test
data: childcareSG_ppp
R = 0.53532, p-value < 2.2e-16
alternative hypothesis: clustered (R < 1)Statistical Conclusion:
Business Insights from above analysis:
Performing Clark_Evans with CSR
clarkevans.test(childcareSG_ppp,
correction="none",
clipregion="sg_owin",
alternative=c("clustered"),
method="MonteCarlo",
nsim=99)
Clark-Evans test
No edge correction
Monte Carlo test based on 99 simulations of CSR with fixed n
data: childcareSG_ppp
R = 0.53532, p-value = 0.01
alternative hypothesis: clustered (R < 1)Statistical Conclusion:
Business Insights:
Kernal Density Estimation Method
Working with automatic bandwidth selection method
kde_SG_diggle <- density(
childcareSG_ppp,
sigma=bw.diggle,
edge=TRUE,
kernel="gaussian") plot(kde_SG_diggle)
summary(kde_SG_diggle)real-valued pixel image
128 x 128 pixel array (ny, nx)
enclosing rectangle: [2667.538, 55941.94] x [21448.47, 50256.33] units
dimensions of each pixel: 416 x 225.0614 units
Image is defined on a subset of the rectangular grid
Subset area = 669941961.12249 square units
Subset area fraction = 0.437
Pixel values (inside window):
range = [-6.584123e-21, 3.063698e-05]
integral = 1927.788
mean = 2.877545e-06we are retrieving the bandwidth which used to compute the kde layer.
bw <- bw.diggle(childcareSG_ppp)
bw sigma
295.9712 Rescalling KDE values
childcareSG_ppp_km <- rescale.ppp(
childcareSG_ppp, 1000, "km")kde_childcareSG_km <- density(childcareSG_ppp_km,
sigma=bw.diggle,
edge=TRUE,
kernel="gaussian")plot(kde_childcareSG_km)
working with different automatic bandwidth methods
bw.CvL(childcareSG_ppp_km) sigma
4.357209 bw.scott(childcareSG_ppp_km) sigma.x sigma.y
2.159749 1.396455 bw.ppl(childcareSG_ppp_km) sigma
0.378997 bw.diggle(childcareSG_ppp_km) sigma
0.2959712 kde_childcareSG.ppl <- density(childcareSG_ppp_km,
sigma=bw.ppl,
edge=TRUE,
kernel="gaussian")
par(mfrow=c(1,2))
plot(kde_childcareSG_km, main = "bw.diggle")
plot(kde_childcareSG.ppl, main = "bw.ppl")
Working with differnet kernel methods
par(mfrow=c(2,2))
plot(density(childcareSG_ppp_km,
sigma=0.2959712,
edge=TRUE,
kernel="gaussian"),
main="Gaussian")
plot(density(childcareSG_ppp_km,
sigma=0.2959712,
edge=TRUE,
kernel="epanechnikov"),
main="Epanechnikov")
plot(density(childcareSG_ppp_km,
sigma=0.2959712,
edge=TRUE,
kernel="quartic"),
main="Quartic")
plot(density(childcareSG_ppp_km,
sigma=0.2959712,
edge=TRUE,
kernel="disc"),
main="Disc")
Fixed and adaptive KDE
Computing KDE bu using fixed bandwidth
We will compute a KDE layer by defining a bandwidth of 600 meter. Notice that in the code chunk below, the sigma value used is 0.6. This is because the unit of measurement of childcareSG_ppp_km object is in kilometer, hence the 600m is 0.6km.
kde_childcareSG_fb <- density(childcareSG_ppp_km,
sigma=0.6,
edge=TRUE,
kernel="gaussian")
plot(kde_childcareSG_fb)
Computing KDE by using adaptive bandwidth
Fixed bandwidth method is very sensitive to highly skew distribution of spatial point patterns over geographical units for example urban versus rural. One way to overcome this problem is by using adaptive bandwidth instead.
In this section, you will learn how to derive adaptive kernel density estimation by using density.adaptive() of spatstat.
kde_childcareSG_ab <- adaptive.density(
childcareSG_ppp_km,
method="kernel")
plot(kde_childcareSG_ab)
Now, comparing both KDE fixed and adaptive.
par(mfrow=c(1,2))
plot(kde_childcareSG_fb, main = "Fixed bandwidth")
plot(kde_childcareSG_ab, main = "Adaptive bandwidth")
Plotting cartographic quality KDE map
Converting gridded output into raster
kde_childcareSG_bw_terra <- rast(kde_childcareSG_km)class(kde_childcareSG_bw_terra)[1] "SpatRaster"
attr(,"package")
[1] "terra"kde_childcareSG_bw_terraclass : SpatRaster
size : 128, 128, 1 (nrow, ncol, nlyr)
resolution : 0.4162063, 0.2250614 (x, y)
extent : 2.667538, 55.94194, 21.44847, 50.25633 (xmin, xmax, ymin, ymax)
coord. ref. :
source(s) : memory
name : lyr.1
min value : -5.824417e-15
max value : 3.063698e+01
unit : km Assigning projection systems
crs(kde_childcareSG_bw_terra) <- "EPSG:3414"kde_childcareSG_bw_terraclass : SpatRaster
size : 128, 128, 1 (nrow, ncol, nlyr)
resolution : 0.4162063, 0.2250614 (x, y)
extent : 2.667538, 55.94194, 21.44847, 50.25633 (xmin, xmax, ymin, ymax)
coord. ref. : SVY21 / Singapore TM (EPSG:3414)
source(s) : memory
name : lyr.1
min value : -5.824417e-15
max value : 3.063698e+01
unit : km Plotting KDE map with tmap
tm_shape(kde_childcareSG_bw_terra) +
tm_raster(col.scale =
tm_scale_continuous(
values = "viridis"),
col.legend = tm_legend(
title = "Density values",
title.size = 0.7,
text.size = 0.7,
bg.color = "white",
bg.alpha = 0.7,
position = tm_pos_in(
"right", "bottom"),
frame = TRUE)) +
tm_graticules(labels.size = 0.7) +
tm_compass() +
tm_layout(scale = 1.0)First Order SPPA at the Planning Subzone Level
Geospatial data wrangling
Extracting Study area
pg <- mpsz_cl %>%
filter(PLN_AREA_N == "PUNGGOL")
tm <- mpsz_cl %>%
filter(PLN_AREA_N == "TAMPINES")
ck <- mpsz_cl %>%
filter(PLN_AREA_N == "CHOA CHU KANG")
jw <- mpsz_cl %>%
filter(PLN_AREA_N == "JURONG WEST")par(mfrow=c(1,1))
plot(st_geometry(pg), main = "Ponggol")
plot(st_geometry(tm), main = "Tampines")
plot(st_geometry(ck), main = "Choa Chu Kang")
plot(st_geometry(jw), main = "Jurong West")
Creating OWIN Object
pg_owin = as.owin(pg)
tm_owin = as.owin(tm)
ck_owin = as.owin(ck)
jw_owin = as.owin(jw)Combining point events object and owin object
childcare_pg_ppp = childcare_ppp[pg_owin]
childcare_tm_ppp = childcare_ppp[tm_owin]
childcare_ck_ppp = childcare_ppp[ck_owin]
childcare_jw_ppp = childcare_ppp[jw_owin]childcare_pg_ppp.km = rescale.ppp(childcare_pg_ppp, 1000, "km")
childcare_tm_ppp.km = rescale.ppp(childcare_tm_ppp, 1000, "km")
childcare_ck_ppp.km = rescale.ppp(childcare_ck_ppp, 1000, "km")
childcare_jw_ppp.km = rescale.ppp(childcare_jw_ppp, 1000, "km")par(mfrow=c(1,1))
plot(unmark(childcare_pg_ppp.km),
main="Punggol")
plot(unmark(childcare_tm_ppp.km),
main="Tampines")
plot(unmark(childcare_ck_ppp.km),
main="Choa Chu Kang")
plot(unmark(childcare_jw_ppp.km),
main="Jurong West")